On the Convergence Rate of Kernel-Based Sequential Greedy Regression

[摘要] A kernel-based greedy algorithm is presented to realize efficient sparse learning with data-dependent basis functions. Upper bound of generalization error is obtained based on complexity measure of hypothesis space with covering numbers. A careful analysis shows the error has a satisfactory decay rate under mild conditions.

[发布日期] 2012-12-13